This issue continues and builds on the design & experimental work already done in LBAF #44, to implement a measure of persistence, for instance defined along those lines:
The Coefficient of Variation (CoV) appears to work well for our purpose, when computed over a sliding window. This is the ratio of stdev/mean (assuming mean >0 which shall always be the case for us as no object will have 0-time associated to it).
There is more “accumulated variation” as the size of the sliding window increases, which is to be expected, while at the same time the CoV becomes smoother. But in all case they seem to yield comparable results. So we should favor the smaller windows as they are faster to compute.
My proposal for a measure of persistence : coefficient of variation (which we can maintain locally on each object at very limited cost, say over a 4-long sliding window), averaged out across all objects each time we want to attempt a LB stage. If it’s greater than 10%, then pass (postpone) the LB. Maybe postpone it by at least the length of the sliding window, to avoid unnecessary global averaging.
This issue continues and builds on the design & experimental work already done in LBAF #44, to implement a measure of persistence, for instance defined along those lines:
The Coefficient of Variation (CoV) appears to work well for our purpose, when computed over a sliding window. This is the ratio of stdev/mean (assuming mean >0 which shall always be the case for us as no object will have 0-time associated to it).
There is more “accumulated variation” as the size of the sliding window increases, which is to be expected, while at the same time the CoV becomes smoother. But in all case they seem to yield comparable results. So we should favor the smaller windows as they are faster to compute.
My proposal for a measure of persistence : coefficient of variation (which we can maintain locally on each object at very limited cost, say over a 4-long sliding window), averaged out across all objects each time we want to attempt a LB stage. If it’s greater than 10%, then pass (postpone) the LB. Maybe postpone it by at least the length of the sliding window, to avoid unnecessary global averaging.